通过先进的AI驱动超声技术提高乳腺癌检测:对Vis-BUS的全面评估
Hyuksool Kwon1,2,3, Seok Hwan Oh1,3,4, Myeong-Gee Kim1,3,4
1Laboratory of Quantitative Ultrasound Imaging, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea.
Diagnostics (Basel, Switzerland)
|September 14, 2024
概括
这项研究介绍了Vis-BUS,这是一种用于乳腺癌检测的AI工具,在区分良性病变和恶性病变方面实现了高准确性. 对于损伤密度和化分析,需要进一步细化.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 乳腺癌仍然是全球主要的死亡原因.
- 准确和早期检测对于有效的治疗和改善患者结果至关重要.
- 目前的诊断方法,包括超声波,可以从AI驱动的改进中受益.
研究的目的:
- 评估Vis-BUS的诊断性能,这是一种用于乳腺癌检测的AI驱动超声波工具.
- 评估Vis-BUS区分良性和恶性乳腺病变的能力.
- 确定影响基于AI的乳腺病变分类准确性的因素.
主要方法:
- 258次乳房超声波检查的回顾性分析.
- 损伤检测AI (LD-AI) 和损伤分析AI (LA-AI) 应用于b模式超声图像的应用.
- 使用AUROC和AUPRC生成癌症概率得分 (CPS) 和评估诊断准确性.
主要成果:
- 维斯巴斯显示了高的诊断准确性,AUROC为0.964和AUPRC为0.967.
- 诸如"脂肪"病变密度 (OR 27.7781) 和BI-RADS类别C5 (OR 10.173) 等因素显著影响了分类的准确性.
- 损伤大小和"未知"密度与较低的分类准确性有关,而微化显示出中等的影响.
结论:
- 在乳腺癌查中,Vis-BUS显著提高了诊断精度.
- 人工智能工具显示出强大的潜力来支持临床决策.
- 建议进一步优化损伤密度表征和微化检测,以提高整体性能.
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